A public opinion analysis model based on Danmu data monitoring and sentiment classification

doi:10.3969/j.issn.1000-5641.2019.03.010

Abstract

Abstract: With the rapid development of online video platforms, Danmu has gradually become an important way for people to express their opinions, and it is particularly welcomed by young people. Unlike conventional texts, Danmu texts are generally short, unstructured, and involve Internet slang as well as conventional stop words to express emotions. In this paper, a public opinion analysis model based on Danmu data is proposed. According to the data generation and storage characteristics of Danmu, a hotspot detection-based loop algorithm is proposed for Danmu data collection. Moreover, the sentiment dictionary to distinguish emotional tendencies is expanded to include network vocabularies commonly appearing in Danmu. Finally, based on the convolutional neural network (CNN), we build a classification model to distinguish positive and negative emotions. Experiments show that the public opinion analysis model of this paper can effectively demonstrate public opinion analysis of Danmu data.

Key words: Danmu emotion, Internet sentiment, emotion classification, deep learning, web crawler

CLC Number:

TP391

YE Jian, ZHAO Hui. A public opinion analysis model based on Danmu data monitoring and sentiment classification[J]. Journal of East China Normal University(Natural Sc, 2019, 2019(3): 86-100.

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